Description
Deployment of Machine Learning Models, the training course on the deployment of machine learning models has been published by Yudemy Academy. This course shows you how to take your machine learning models from a research environment to a fully integrated production environment. Implementation of machine learning models or in simpler terms, implementation of models means making your models available to other systems within the organization or the web so that they can receive data and make their predictions. return By applying machine learning models, you can make the most of the model you have created.
In this article, we are going to introduce you to interesting video tutorials and teach you everything you need to start building a model in a research environment, and then convert Jupyter notebooks into production code, package the code. And put in an API and add continuous integration and continuous delivery to it. We’ll discuss the concept of reproducibility, why it’s important, and how to maximize reproducibility during deployment through version control, code repositories, and the use of Docker, as well as the tools and platforms available for deploying machine learning models. we will
What you will learn
- Building machine learning model APIs and deploying models in the cloud
- Send and receive requests from deployed machine learning models
- Designing testable and version controlled and repeatable code for model deployment
- Create continuous and automated integrations to deploy your models
- Know the optimal machine learning architecture
- Know the different resources available to produce your models
- Identifying and reducing the challenges of producing models
Who is this course suitable for?
- Data scientists who want to implement their first machine learning model
- Data scientists who want to learn best practices for model deployment
- Software developers who want to enter the world of machine learning
Description of the Deployment of Machine Learning Models course
- Publisher: Udemy
- teacher: Soledad Galli , Christopher Samiullah
- English language
- Education level: Intermediate
- Number of courses: 151
- Training duration: 10 hours and 26 minutes
Head of the seasons
Course prerequisites
- A Python installation
- A Git installation
- Confidence in Python programming, including familiarity with Numpy, Pandas and Scikit-learn
- Familiarity with the use of IDEs, like Pycharm, Sublime, Spyder or similar
- Familiarity with writing Python scripts and running them from the command line interface
- Knowledge of basic git commands, including clone, fork, branch creation and branch checkout
- Knowledge of basic git commands, including git status, git add, git commit, git pull, git push
- Knowledge of basic CLI commands, including navigating folders and using Git and Python from the CLI
- Knowledge of Linear Regression and model evaluation metrics like the MSE and R2
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 1080p
Changes:
Version 2/2023 compared to 2021/5 has increased the number of 11 lessons and the duration of 50 minutes. Also, the course quality has been increased from 720p to 1080p.
download link
File(s) password: www.downloadly.ir
Size
4.61 GB
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